Productivity versus Endowments: A Study of Singapore's Sectoral Growth, 1974-92
Bibliographic record
Abstract
Productivity and the Rybczynski effects of factor endowments have been highlighted as the two main reasons behind the growth of the East Asian NIEs. However, empirical studies at the aggregate level do not find support for the former. Focusing on Singapore's manufacturing industries, this paper estimates the contributions of these two factors to sectoral growth. The results show that both productivity and factor endowments are important. The contributions of factor endowments are larger than that of productivity growth for the non-electronics industries, while productivity dominates factor endowments as the most important source of growth in the electronics industry. (JEL 047, F43, L60) *Development Research Group, The World Bank, MSN: MC8-810, 1818 H Street, N.W., Washington, DC 20433. Tel: (202) 473 4155, Fax: (202) 522 1557, E-mail: hlkee@worldbank.org. I would like to give special thanks to Robert Feenstra for his insightful guidance and comments. Discussions with Lee Branstetter, Deborah Swenson, and Gary Hunt are gratefully acknowledged. I am also indebted to all seminar participants in the Western Economic Association International Conference 2000, University of Alberta, University of Colorado-Denver, Franklin and Marshall College, University of Georgia, University of Maine, Mount Holyoke College, University of Notre Dame, National University of Singapore, University of Western Michigan, University of Virginia and the World Bank.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".